Import the Stackdriver Profiler package, and configure it to relay function timing data to Stackdriver for further analysis.

Import the Stackdriver Debugger package, and configure the application to emit debug messages with timing information.

Instrument the code using a timing library, and publish the metrics via a health check endpoint that is scraped by Stackdriver.

Install an Application Performance Monitoring (APM) tool in both locations, and
configure an export to a central data storage location for analysis.

Roll back the recent release.

Review the Stackdriver monitoring.

Upsize the virtual machines running the login services.

Deploy a new release to see whether it fixes the problem

Add logic to each Cloud Build step to HTTP POST the build information to a webhook.

Add a new step at the end of the pipeline in Cloud Build to HTTP POST the build information to a webhook.

Use Stackdriver Logging to create a logs-based metric from the Cloud Buitd logs. Create an Alert with a Webhook notification type.

Create a Cloud Pub/Sub push subscription to the Cloud Build cloud-builds PubSub topic
to HTTP POST the build information to a webhook.

Look for the agent's test log entry in the Logs Viewer.

Install the most recent version of the Stackdriver agent.

Verify the VM service account access scope includes the monitoring.write scope.

SSH to the VM and execute the following commands on your VM: ps ax I grep fluentd

Develop a postmortem that includes the root causes, resolution, lessons learned, and a prioritized list of action items. Share it with the manager only.

Develop a postmortem that includes the root causes, resolution, lessons learned, and a prioritized list of action items. Share it on the engineering organization's document portal.

Develop a postmortem that includes the root causes, resolution, lessons learned, the list of people responsible, and a list of action items for each person. Share it with the manager
only.

Develop a postmortem that includes the root causes, resolution, lessons learned, the list
of people responsible, and a list of action items for each person. Share it on the
engineering organization's document portal.

• Store your code in a Git-based version control system.
• Establish a process that allows developers to merge their own changes at the end of each
day.
• Package and upload code lo a versioned Cloud Storage bucket as the latest master
version.

• Store your code in a Git-based version control system.
• Establish a process that includes code reviews by peers and unit testing to ensure
integrity and functionality before integration of code.
• Establish a process where the fully integrated code in the repository becomes the latest
master version.

• Store your code as text files in Google Drive in a defined folder structure that organizes
the files.
• At the end of each day. confirm that all changes have been captured in the files within the
folder structure.
• Rename the folder structure with a predefined naming convention that increments the
version.

• Store your code as text files in Google Drive in a defined folder structure that organizes
the files.
• At the end of each day, confirm that all changes have been captured in the files within the
folder structure and create a new .zip archive with a predefined naming convention.
• Upload the .zip archive to a versioned Cloud Storage bucket and accept it as the latest
version.

In the Google Cloud Platform Console, use the Cost Breakdown section to visualize the costs per system.

Assign all instances a label specific to the system they run. Configure BigQuery billing
export and query costs per label.

Enrich all instances with metadata specific to the system they run. Configure Stackdriver
Logging to export to BigQuery, and query costs based on the metadata.

Name each virtual machine (VM) after the system it runs. Set up a usage report export
to a Cloud Storage bucket. Configure the bucket as a source in BigQuery to query costs
based on VM name.

Add more serving capacity to all of your application’s zones

Have more frequent or potentially risky application releases.

Tighten the SLO match the application’s observed reliability.

Implement and measure additional Service Level Indicators (SLIs) fro the application.

Announce planned downtime to consume more error budget, and ensure that users are
not depending on a tighter SLO.